R: Blog post frequency anomaly detection

I came across Twitter's anomaly detection library last year but haven't yet had a reason to take it for a test run so having got my blog post frequency data into shape I thought it'd be fun to run it through the algorithm.

I wanted to see if it would detect any periods of time when the number of posts differed significantly - I don't really have an action I'm going to take based on the results, it's curiosity more than anything else!

First we need to get the library installed. It's not on CRAN so we need to use devtools to install it from the github repository:
~~~
~~~r
install.packages("devtools")
devtools::install_github("twitter/AnomalyDetection")
library(AnomalyDetection)
~~~

The expected data format is two columns - one containing a time stamp and the other a count. e.g. using the 'raw_data' data frame that is in scope when you add the library:

Interestingly I don't seem to have any low end anomalies - there were a couple of really high frequency weeks when I first started writing and I think one of the other weeks contains a New Year's Eve when I was particularly bored!

If we group by month instead only the very first month stands out as an outlier: